The sequencing of the human genome a decade ago fueled a computational revolution in biology, which has arguably been an impetus for more new algorithms than any other fundamental realm of science. The newly formed links between computer science and biology affect the way we teach computational ideas to biologists, as well as how applied algorithms are taught to computer scientists. Genome sequencing is just one of hundreds of biological problems that have become inextricable from the computational methods required to solve them. In this course, we will take a look at some of the algorithmic ideas that are fundamental to an understanding of modern biology. Computational concepts like dynamic programming and graph theory will help us explore algorithms applied to a wide range of biological topics, from finding regulatory motifs to reconstructing the tree of life. Throughout the process, we will apply real bioinformatics algorithms to real genetic data.

We have created an interactive textbook for this course, which allows you to learn and solve problems concurrently. This textbook will be hosted on Stepic, a new resource for learning interactively.

Course Syllabus
Note: the syllabus may undergo revisions throughout the course.

Each homework will contain 5-10 programming assignments.

The course will be based around the following central questions, with the algorithmic ideas that we will use to solve them in parentheses:

Where Does DNA Replication Begin? (Algorithmic Warm-up)
How Do We Sequence Antibiotics? (Brute Force Algorithms)
Which DNA Patterns Act As Cellular Clocks? (Greedy and Randomized Algorithms)
How Do We Assemble Genomes? (Graph Algorithms)
Are There Fragile Regions in the Human Genome? (Combinatorial Algorithms)
How Do We Compare Biological Sequences? (Dynamic Programming Algorithms)
How Do We Locate Disease-Causing Mutations? (Combinatorial Pattern Matching)

You can find a more detailed version of this syllabus here.

Bioinformatics Algorithms (Part 2) will start in Spring 2014 and will cover: gene expression analysis, evolutionary tree reconstruction, computational aspects of human genetics, computational proteomics, genome annotation, network biology, and many other topics.

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